285 related articles for article (PubMed ID: 34660284)
1. Application of Deep Convolution Network to Automated Image Segmentation of Chest CT for Patients With Tumor.
Xie H; Zhang JF; Li Q
Front Oncol; 2021; 11():719398. PubMed ID: 34660284
[TBL] [Abstract][Full Text] [Related]
2. Automated segmentation of the left ventricle from MR cine imaging based on deep learning architecture.
Qin W; Wu Y; Li S; Chen Y; Yang Y; Liu X; Zheng H; Liang D; Hu Z
Biomed Phys Eng Express; 2020 Feb; 6(2):025009. PubMed ID: 33438635
[TBL] [Abstract][Full Text] [Related]
3. Deep-learning-based detection and segmentation of organs at risk in nasopharyngeal carcinoma computed tomographic images for radiotherapy planning.
Liang S; Tang F; Huang X; Yang K; Zhong T; Hu R; Liu S; Yuan X; Zhang Y
Eur Radiol; 2019 Apr; 29(4):1961-1967. PubMed ID: 30302589
[TBL] [Abstract][Full Text] [Related]
4. Improved accuracy of auto-segmentation of organs at risk in radiotherapy planning for nasopharyngeal carcinoma based on fully convolutional neural network deep learning.
Peng Y; Liu Y; Shen G; Chen Z; Chen M; Miao J; Zhao C; Deng J; Qi Z; Deng X
Oral Oncol; 2023 Jan; 136():106261. PubMed ID: 36446186
[TBL] [Abstract][Full Text] [Related]
5. Automatic liver segmentation by integrating fully convolutional networks into active contour models.
Guo X; Schwartz LH; Zhao B
Med Phys; 2019 Oct; 46(10):4455-4469. PubMed ID: 31356688
[TBL] [Abstract][Full Text] [Related]
6. Segmentation of liver tumors with abdominal computed tomography using fully convolutional networks.
Chen CI; Lu NH; Huang YH; Liu KY; Hsu SY; Matsushima A; Wang YM; Chen TB
J Xray Sci Technol; 2022; 30(5):953-966. PubMed ID: 35754254
[TBL] [Abstract][Full Text] [Related]
7. Fully convolutional multi-scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers.
Khened M; Kollerathu VA; Krishnamurthi G
Med Image Anal; 2019 Jan; 51():21-45. PubMed ID: 30390512
[TBL] [Abstract][Full Text] [Related]
8. Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks.
Tong N; Gou S; Yang S; Ruan D; Sheng K
Med Phys; 2018 Oct; 45(10):4558-4567. PubMed ID: 30136285
[TBL] [Abstract][Full Text] [Related]
9. An application of cascaded 3D fully convolutional networks for medical image segmentation.
Roth HR; Oda H; Zhou X; Shimizu N; Yang Y; Hayashi Y; Oda M; Fujiwara M; Misawa K; Mori K
Comput Med Imaging Graph; 2018 Jun; 66():90-99. PubMed ID: 29573583
[TBL] [Abstract][Full Text] [Related]
10. Fully Automated Gross Tumor Volume Delineation From PET in Head and Neck Cancer Using Deep Learning Algorithms.
Shiri I; Arabi H; Sanaat A; Jenabi E; Becker M; Zaidi H
Clin Nucl Med; 2021 Nov; 46(11):872-883. PubMed ID: 34238799
[TBL] [Abstract][Full Text] [Related]
11. Deep convolutional neural network for segmentation of thoracic organs-at-risk using cropped 3D images.
Feng X; Qing K; Tustison NJ; Meyer CH; Chen Q
Med Phys; 2019 May; 46(5):2169-2180. PubMed ID: 30830685
[TBL] [Abstract][Full Text] [Related]
12. Fully connected network with multi-scale dilation convolution module in evaluating atrial septal defect based on MRI segmentation.
Chen H; Yan S; Xie M; Ye Y; Ye Y; Zhu D; Su L; Huang J
Comput Methods Programs Biomed; 2022 Mar; 215():106608. PubMed ID: 35063713
[TBL] [Abstract][Full Text] [Related]
13. Double-branched and area-constraint fully convolutional networks for automated serous retinal detachment segmentation in SD-OCT images.
Gao K; Niu S; Ji Z; Wu M; Chen Q; Xu R; Yuan S; Fan W; Chen Y; Dong J
Comput Methods Programs Biomed; 2019 Jul; 176():69-80. PubMed ID: 31200913
[TBL] [Abstract][Full Text] [Related]
14. Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method.
Zhou X; Takayama R; Wang S; Hara T; Fujita H
Med Phys; 2017 Oct; 44(10):5221-5233. PubMed ID: 28730602
[TBL] [Abstract][Full Text] [Related]
15. Shape constrained fully convolutional DenseNet with adversarial training for multiorgan segmentation on head and neck CT and low-field MR images.
Tong N; Gou S; Yang S; Cao M; Sheng K
Med Phys; 2019 Jun; 46(6):2669-2682. PubMed ID: 31002188
[TBL] [Abstract][Full Text] [Related]
16. Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks.
Ibragimov B; Xing L
Med Phys; 2017 Feb; 44(2):547-557. PubMed ID: 28205307
[TBL] [Abstract][Full Text] [Related]
17. AnatomyNet: Deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy.
Zhu W; Huang Y; Zeng L; Chen X; Liu Y; Qian Z; Du N; Fan W; Xie X
Med Phys; 2019 Feb; 46(2):576-589. PubMed ID: 30480818
[TBL] [Abstract][Full Text] [Related]
18. [Application of Improved Unet Network in the Recognition and Segmentation of Hemorrhage Regions in Brain CT Images].
Zhou ZS; Chen XM; Zhang HY; Wan HL; Zhao JY; Zhang T; Wang XY
Sichuan Da Xue Xue Bao Yi Xue Ban; 2022 Jan; 53(1):114-120. PubMed ID: 35048610
[TBL] [Abstract][Full Text] [Related]
19. A coronary artery CTA segmentation approach based on deep learning.
Huang C; Yin C
J Xray Sci Technol; 2022; 30(2):245-259. PubMed ID: 34957947
[TBL] [Abstract][Full Text] [Related]
20. Automated deep learning-based segmentation of COVID-19 lesions from chest computed tomography images.
Salehi M; Ardekani MA; Taramsari AB; Ghaffari H; Haghparast M
Pol J Radiol; 2022; 87():e478-e486. PubMed ID: 36091652
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]